Riemannian approaches in brain-computer interfaces: a review
Although promising from numerous applications, current brain-computer interfaces (BCIs)
still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and …
still suffer from a number of limitations. In particular, they are sensitive to noise, outliers and …
Performance variation in motor imagery brain–computer interface: a brief review
Brain–computer interface (BCI) technology has attracted significant attention over recent
decades, and has made remarkable progress. However, BCI still faces a critical hurdle, in …
decades, and has made remarkable progress. However, BCI still faces a critical hurdle, in …
A convolutional neural network for steady state visual evoked potential classification under ambulatory environment
The robust analysis of neural signals is a challenging problem. Here, we contribute a
convolutional neural network (CNN) for the robust classification of a steady-state visual …
convolutional neural network (CNN) for the robust classification of a steady-state visual …
Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm
Variation in human brains creates difficulty in implementing electroencephalography into
universal brain–machine interfaces. Conventional electroencephalography systems typically …
universal brain–machine interfaces. Conventional electroencephalography systems typically …
A lower limb exoskeleton control system based on steady state visual evoked potentials
Objective. We have developed an asynchronous brain–machine interface (BMI)-based
lower limb exoskeleton control system based on steady-state visual evoked potentials …
lower limb exoskeleton control system based on steady-state visual evoked potentials …
Correcting robot mistakes in real time using EEG signals
Communication with a robot using brain activity from a human collaborator could provide a
direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide …
direct and fast feedback loop that is easy and natural for the human, thereby enabling a wide …
L1-regularized multiway canonical correlation analysis for SSVEP-based BCI
Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and
designed reference signals of sine-cosine waves usually works well for steady-state visual …
designed reference signals of sine-cosine waves usually works well for steady-state visual …
Classification of multiple motor imagery using deep convolutional neural networks and spatial filters
BE Olivas-Padilla, MI Chacon-Murguia - Applied Soft Computing, 2019 - Elsevier
Abstract Brain–Computer Interfaces (BCI) are systems that translate brain activity patterns
into commands for an interactive application, and some of them recognize patterns …
into commands for an interactive application, and some of them recognize patterns …
Toward EEG-based BCI applications for industry 4.0: Challenges and possible applications
K Douibi, S Le Bars, A Lemontey, L Nag… - Frontiers in Human …, 2021 - frontiersin.org
In the last few decades, Brain-Computer Interface (BCI) research has focused predominantly
on clinical applications, notably to enable severely disabled people to interact with the …
on clinical applications, notably to enable severely disabled people to interact with the …
The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users
This work aims at corroborating the importance and efficacy of mutual learning in motor
imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our …
imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our …